Cognition-based networks: A new perspective on network optimization using learning and distributed intelligence

Michele Zorzi, Andrea Zanella, Alberto Testolin, Michele De Filippo De Grazia, Marco Zorzi

Research output: Contribution to journalArticlepeer-review


In response to the new challenges in the design and operation of communication networks, and taking inspiration from how living beings deal with complexity and scalability, in this paper we introduce an innovative system concept called COgnition-BAsed NETworkS (COBANETS). The proposed approach develops around the systematic application of advanced machine learning techniques and, in particular, unsupervised deep learning and probabilistic generative models for system-wide learning, modeling, optimization, and data representation. Moreover, in COBANETS, we propose to combine this learning architecture with the emerging network virtualization paradigms, which make it possible to actuate automatic optimization and reconfiguration strategies at the system level, thus fully unleashing the potential of the learning approach. Compared with the past and current research efforts in this area, the technical approach outlined in this paper is deeply interdisciplinary and more comprehensive, calling for the synergic combination of expertise of computer scientists, communications and networking engineers, and cognitive scientists, with the ultimate aim of breaking new ground through a profound rethinking of how the modern understanding of cognition can be used in the management and optimization of telecommunication networks.

Original languageEnglish
Article number7217798
Pages (from-to)1512-1530
Number of pages19
JournalIEEE Access
Publication statusPublished - 2015


  • Cognitive networks
  • deep learning
  • hierarchical generative models
  • optimization

ASJC Scopus subject areas

  • Computer Science(all)
  • Engineering(all)
  • Materials Science(all)


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